Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML-and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML-and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS’s). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-of-sight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning.

Resource allocation and spectrum management are two of the major challenges in the massive scale deployment of Machine-to-Machine (M2M) communication and Internet of Things (IoT) networks. Existence of large number of devices per unit area leads to the problem of congestion, network overload and deterioration of the Signal to Noise Ratio (SNR). These problems can be eliminated through efficient resource allocation mechanisms which also play pivotal role in optimizing the throughput, delay, and power consumption of a network. Most of the existing resource allocation mechanisms are centralized and do not gracefully support the heterogeneous and dynamic IoT networks. Therefore, distributed and Machine Learning- (ML) based approaches are essential. Catering massive number of smart devices become challenging for the traditional distributed methods whereas ML-based approaches are currently scarce in the literature. To fill the voids, we propose a new distributed, block based Q-learning algorithm for slot scheduling of the smart devices / Machine Type Communication Devices (MTCDs) in the clustered IoT networks. We furthermore, propose various reward schemes for the evolution of Q-values in the proposed scheme and, discuss and evaluate its effect on the distributed model. The goal is to avoid inter-cluster and intra-cluster interference, and also to improve the Signal to Interference Ratio (SIR) by employing frequency diversity in a multi-channel system. Through extensive simulations, we analyze the effects of the distributed slotassignment with respect to varying SIR on the convergence rate and the convergence probability. Our theoretical analysis and simulations validate the effectiveness of our proposed method such that: (i) each MTCD is allocated with a suitable slot with acceptable SIR levels, (ii) IoT network can efficiently converge to a collision-free transmission causing least intracluster interferences, through each MTCD's learning ability in the process of the distributed slot allocation.

Calamities like tsunami and flood cause communication outage by damaging the communication infrastructure and power supply. As an alternate communication facility, multi-hop relay networks are deployed. Deploying these networks is quick and easy, however, the performance is deteriorated due to the use of relays in such environments. Similarly, given the surge in bandwidth hungry applications, performance parameters such as high throughput and low end-to-end delay are also daunting challenges in the traditional one-hop wireless LANs (WLANs). In this context, simultaneous transmission over multiple bands has the potential to improve the capacity of relay networks and accommodate the future unforeseen bandwidth-hungry applications. Manipulating the attributive diversity of different bands will yield prolific gains in terms of performance. However, for multi-hop transmissions in such multi- band networks, channel conditions and bit-rates over different bands, different links tend to be different. This causes significant degradation in the performance in terms of throughput and end-to- end delay. To address this problem, considering the channel conditions and bitrates of each band on either side of the relay, we exploit Shapley value to distribute the load in such a way that maximizes throughput and minimizes end-to-end delay in multi- band networks. Illustrative numerical results demonstrate the effectiveness of our proposed scheme.